#!/usr/bin/python
# -*- coding: utf-8 -*-
# Author = 'IReverser'

import torch
import torch.nn.functional as F
from torch.autograd import Variable
import matplotlib.pyplot as plt
import time

n_data = torch.ones([100, 2])
x0 = torch.normal(2*n_data, 1)   # class0 x data (tensor), shape=(100, 2)
y0 = torch.zeros(100)            # class0 y data (tensor), shape=(100, 1)
x1 = torch.normal(-2*n_data, 1)  # class1 x data (tensor), shape=(100, 2)
y1 = torch.ones(100)             # class1 y data (tensor), shape=(100, 1)
x = torch.cat((x0, x1), 0).type(torch.FloatTensor)  # FloatTensor = 32-bit floating
y = torch.cat((y0, y1), ).type(torch.LongTensor)   # LongTensor = 64-bit integer


# The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors
# x, y = Variable(x), Variable(y)
#
# plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=y.data.numpy(), s=100, lw=0, cmap='RdYlGn')
# plt.show()

# method 1
class Net(torch.nn.Module):
    def __init__(self, n_feature, n_hidden, n_output):
        super(Net, self).__init__()
        self.hidden = torch.nn.Linear(n_feature, n_hidden)
        self.predict = torch.nn.Linear(n_hidden, n_output)

    def forward(self, x):
        x = F.relu(self.hidden(x))
        x = self.predict(x)
        return x


net1 = Net(2, 10, 2)

# method 2
net2 = torch.nn.Sequential(
    torch.nn.Linear(2, 10),
    torch.nn.ReLU(),
    torch.nn.Linear(10, 2),
)

print(net1)
print(net2)

# optimizer = torch.optim.SGD(net.parameters(), lr=0.02)
# loss_func = torch.nn.CrossEntropyLoss()
#
# start_time = time.time()
# plt.ion()
#
# for t in range(150):
#     print(t)
#     out = net(x)    # [-2, -.12, 20] F,softmax(out) [.1, 0.2, .7]
#
#     loss = loss_func(out, y)   # must be (1. nn output, 2. target), the target label is NOT one-hotted
#
#     optimizer.zero_grad()   # set gradient of parameters to zero
#     loss.backward()         # calculate gradient of per node
#     optimizer.step()    # optimize gradient
#
#     if t % 2 == 0:
#         # plot and show learning process
#         plt.cla()
#         prediction = torch.max(out, 1)[1]
#         pred_y = prediction.data.numpy()
#         target_y = y.data.numpy()
#         plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=pred_y, s=100, lw=0, cmap='RdYlGn')
#         accuracy = float((pred_y == target_y).astype(int).sum()) / float(target_y.size)
#         plt.text(1.5, -4, 'Accuracy=%.2f' % accuracy, fontdict={'size': 20, 'color':  'red'})
#         plt.pause(0.1)
#
# end_time = time.time()
# print('The total cost time:', str(end_time - start_time) + 'sec')
# plt.ioff()
# plt.show()




























